Start by defining the task, language variety and script—not by choosing a GPU. Translation, text generation, transliteration, speech recognition, speech synthesis and OCR need different data and evaluation. First check whether an existing model or dataset fits; fine-tune or build from scratch only after you know what is missing.
1. Define what the model must do
“An Indian-language AI model” is not one technical target. Decide what input the system will receive, what output it must produce and how you will judge whether it works. BHASHINI describes language services across multiple task categories, but a platform listing alone does not establish that a particular resource is suitable for your project.
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- Translation: specify the source and target languages, direction, domain and whether the output should be formal, conversational or both.
- Text generation or question answering: identify the language, subject area, expected response style and whether the model should use a defined source of information.
- Transliteration: specify the source and destination scripts and how the system should handle names, ambiguous spellings and mixed-script text.
- Speech recognition or synthesis: determine whether the target is spoken input or generated audio, and account for the language and speech variety your users actually use.
- OCR: identify the script and the kinds of images or documents to process.
Also record the language variety, script conventions, domain, audience and deployment constraints. These choices determine which data, existing model and evaluation method are relevant.
2. Look for an existing dataset or model first
Search established Indic-language projects before collecting a corpus yourself. AI4Bharat describes work across India’s 22 constitutionally recognized languages and identifies tools for data work; BHASHINI describes access to language APIs, models, datasets, glossaries, developer tools and services. Check each resource’s current language coverage, task, version, access requirements and terms rather than assuming that a platform’s overall coverage applies to every item it lists.
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| Resource | What it can help you investigate | What to verify |
|---|---|---|
| AI4Bharat project page | Indic-language models and related work; the page also identifies Setu for large-scale crawling and data cleaning. | Whether a particular model, tool or dataset supports your task, language, script and intended use. |
| AI4Bharat AI Tools | Tools and resources from AI4Bharat’s National Language Translation Mission Data Management Unit work, including datasets, models and AI tools. | Current resource details, access and applicable terms. |
| BHASHINI | A government platform describing access to APIs, models, datasets, glossaries, developer tools, language services and enterprise support. | Availability and suitability of the specific service or resource; platform-level descriptions do not establish individual dataset quality. |
| IndicLLMSuite | A resource to investigate for Indic-language pretraining and instruction fine-tuning datasets. | Which included datasets fit your language, task, domain and reuse requirements; the repository’s description is not independent validation of every dataset. |
| IndicTrans2 | A concrete starting point for multilingual Indian-language machine translation, with published data, checkpoints, training and fine-tuning instructions, and evaluation resources. | Language-pair coverage, model and dataset terms, workflow requirements, benchmark fit and deployment constraints. |
For transliteration, the Aksharantar paper reports 26 million transliteration pairs spanning 21 Indic languages and 12 scripts; this is a result reported by the paper’s authors in 2022, not a guarantee that the data fits every transliteration use case. See the Aksharantar paper.
3. Choose between using, fine-tuning and training from scratch
Use an existing model when it fits
If an available model already supports the task, language pair or script you need and performs adequately on representative examples, using it may avoid the data and compute burden of further training. Check its terms and practical access conditions, then evaluate it on your own held-out examples before relying on it.
Fine-tune when you need a targeted change
Fine-tuning is worth investigating when a suitable base model exists but needs adaptation to a domain, language variety, output style or task. The feasibility depends on the model’s training instructions, the data you can use and the available compute. IndicTrans2 publishes fine-tuning workflows for translation; that does not make its training recipe applicable to speech, OCR or general-purpose chat.
Train from scratch only when the case is clear
Training a new model is a separate undertaking from adapting a checkpoint. It requires an appropriate data strategy, a defined evaluation plan and compute sized for the specific architecture and workload. The sources here do not establish a universal threshold at which starting from scratch is preferable, so compare a candidate model against the actual requirements before committing.
4. Build a usable dataset, not just a large one
Data quality and fit affect whether the model learns the behavior you need. Treat collection and preparation as an engineering workflow, and confirm that you have the right to use each source for the planned purpose. Setu is identified by AI4Bharat as a tool for crawling and cleaning, while IndicTrans2 documentation discusses combining appropriate data and deduplicating against benchmark examples.
- Inventory the data. Record language, script, variety, domain, source, format and intended task for each collection. Separate material with different provenance or terms.
- Check access and reuse terms. Review the terms for source content and datasets independently of the terms for any code or model checkpoint you plan to use.
- Normalize consistently. Choose script and text conventions appropriate to the language and task, and apply them consistently. Preserve enough source information to trace and review errors.
- Clean and deduplicate. Remove malformed or irrelevant examples and duplicates. For translation, follow the IndicTrans2 guidance to avoid benchmark overlap; do not allow evaluation material to leak into training data.
- Keep a held-out evaluation set. Set examples aside before training so they can measure performance on data the model did not train on.
- Review with fluent speakers. Inspect representative examples and errors with people who understand the language and its usage, including the varieties relevant to your users.
These are recommended practices, not a single prescribed pipeline for every task. Speech, OCR, translation and text generation have different data formats and quality checks.
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5. Estimate compute for the workload you actually have
Compute requirements depend on whether you are serving a model, fine-tuning a checkpoint or training from scratch. Model architecture and size, sequence length, dataset volume and time budget also affect the estimate. There is no evidence-based universal GPU count, price or training duration for an unspecified Indian-language model.
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| Workload | What to estimate | Practical starting point |
|---|---|---|
| Inference | The model and input/output workload you need to serve, along with the required availability and response time. | Test the intended model and representative requests under the deployment conditions you expect. |
| Fine-tuning | The checkpoint, training data, sequence length, training configuration and time available. | Follow the model’s documented workflow and use a workload-specific compute estimate. |
| Training from scratch | The proposed architecture and size, data volume, training plan and time budget. | Develop an estimate for that defined workload rather than extrapolating from a generic GPU figure. |
IndiaAI’s Compute Portal provides a Ready Reckoner for Compute Users with GPU configuration guidance. Check current portal terms and availability when planning; the guidance does not supply a universal cost or configuration for every model.
6. Evaluate the model on the intended task
Use representative held-out examples and inspect failures with fluent speakers. A model can score well on a benchmark and still fail on the language variety, domain or usage your users care about, so treat benchmark results as one part of the evaluation rather than a substitute for fit testing.
For translation, IndicTrans2 identifies IN22 and FLORES-22 among its evaluation resources, reports chrF++, BLEU and COMET, and describes separate general and conversational benchmark subsets. These are translation-specific examples; the metrics do not establish performance for speech, OCR, transliteration or general chat. Select task-matched tests for other applications.
7. Check terms for every component
Review code, model checkpoints, datasets and source content separately. A repository’s license summary for its code or checkpoint should not be assumed to grant rights over data included in a project or text collected from another source. Confirm the terms for the exact materials and intended use before training or deployment.
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Quick Recap
A practical decision sequence
- Write down the task, language or language pair, scripts, variety, domain and intended deployment.
- Search AI4Bharat, BHASHINI and task-specific resources for a model and data that match those requirements.
- Compare available options on task and language coverage, script and domain fit, benchmark evidence, terms, access and deployment constraints.
- Test the best-fitting candidate on held-out, representative examples and review failures with fluent speakers.
- If the candidate is close but needs adaptation, investigate its documented fine-tuning path; if no candidate fits, define a data and training plan before estimating compute.
- Estimate inference, fine-tuning or training compute for the chosen workload, then confirm current availability, terms and data-handling requirements.
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